CI/CD

Continuous integration builds and tests changes frequently so defects are detected before they accumulate. Continuous delivery keeps validated artifacts ready for release, while continuous deployment automatically promotes changes that pass the defined gates. Pipelines may run unit checks, security scans, integration tests and application evaluations. In AI systems, prompts and model configuration can be versioned inputs alongside code. This entry concerns the general software delivery discipline; ML CI/CD adds data, training and model-validation workflows. A pipeline's reliability depends on the relevance of its gates and the identity of the artifact released, not merely the presence of automation.

The practitioner defines triggers, isolated build environments and checks proportional to the change. Artifacts should be versioned and promoted consistently between environments. Credentials need narrow permissions, and release gates should have clear ownership. Useful outputs include pipeline configuration, check results and deployment records tied to a revision. Rollback must be workable for the deployed state. AI-specific regression cases can detect prompt or model changes that preserve software interfaces while altering user-visible behavior.

Green checks cannot prove behaviors the pipeline never tests. Flaky evaluations can obscure regressions, and a different build at deployment time breaks traceability. Automated deployment also needs a strategy for state changes that rollback cannot simply reverse. Quality requires meaningful gates, controlled secrets and verified artifact identity. CI/CD improves delivery discipline, but it does not substitute for a sound evaluation set or a release decision appropriate to the application's consequences.